gistlib - reinforcement learning for k arm bandit problem in python

To implement reinforcement learning for the k-arm bandit problem in Python, we can use the epsilon-greedy algorithm. Here's a step-by-step guide on how to do it:

First, import the necessary libraries:


main.py
import numpy as np
import matplotlib.pyplot as plt
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Define the k-arm bandit problem by creating a class with the following methods:


main.py
class KArmBandit:
    def __init__(self, k):
        self.k = k
        self.q_true = np.random.normal(0, 1, k)  # True action values
        self.q_estimates = np.zeros(k)  # Estimated action values
        self.action_counts = np.zeros(k)  # Number of times each action is chosen
        
    def act(self, action):
        # Generate a reward with a mean equal to the true action value plus some noise
        reward = np.random.normal(self.q_true[action], 1)
        self.action_counts[action] += 1
        return reward
    
    def get_best_action(self):
        # Choose the action with the highest estimated action value
        best_action = np.argmax(self.q_estimates)
        return best_action
    
    def update_estimates(self, action, reward):
        # Update the estimated action value using the incremental sample-average algorithm
        self.q_estimates[action] += (reward - self.q_estimates[action]) / self.action_counts[action]
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Implement the epsilon-greedy algorithm:


main.py
def epsilon_greedy(k_bandit, epsilon, num_steps):
    rewards = np.zeros(num_steps)
    
    for step in range(num_steps):
        # Decide whether to explore or exploit
        if np.random.uniform() < epsilon:
            # Explore: choose a random action
            action = np.random.choice(range(k_bandit.k))
        else:
            # Exploit: choose the best action
            action = k_bandit.get_best_action()
        
        reward = k_bandit.act(action)
        k_bandit.update_estimates(action, reward)
        
        rewards[step] = reward
    
    return rewards
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Now, you can create an instance of the KArmBandit class and run the epsilon-greedy algorithm:


main.py
k_bandit = KArmBandit(k=10)
rewards = epsilon_greedy(k_bandit, epsilon=0.1, num_steps=1000)

# Plot the rewards over time
plt.plot(rewards)
plt.xlabel('Steps')
plt.ylabel('Average Reward')
plt.title('Epsilon-Greedy Algorithm')
plt.show()
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This code will run the epsilon-greedy algorithm on a k-arm bandit problem with 10 actions, using an exploration parameter of 0.1, and for a total of 1000 steps. The plot will show how the average reward evolves over time.

Note that the example above assumes a stationary k-arm bandit problem, where the true action values do not change over time. In a non-stationary problem, you may need to incorporate additional exploration strategies or change the way the estimated action values are updated.

reinforcement learning for k arm bandit problem in python

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